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We present a new content-based visual information retrieval system that applies different existing techniques in a novel way. We use multi-scale local binary patterns to extract a set of features histogram of an image. Then we integrate it with a superpixel-based saliency model to assign weights to the features. Finally we train a group of constrained similarity boundaries using SVM to exploit the...
This paper presents an objective keyword selection method called visualness with Lesk disambiguation (VLD) for describing educational videos with semantic tags. It extends the work on automatically extracting and associating meaningful keywords carried out in ‘semantic tags for lecture videos’ for efficient indexing and retrieval. VLD uses lecture videos and surrogates documents such as lecture transcripts...
In an effort to develop effective multi-media learning objects (MLO), we propose a framework to extract and associate semantic tags to temporally segmented instructional videos. These tags serve for the purpose of efficient indexing and retrieval system. We create these semantic tags from potential keywords extracted from the lecture transcript. The keywords undergo a series of refinement process...
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